{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(keras.layers.Conv2D(filters=32, kernel_size=(3, 3), padding=\"SAME\", strides=1, \n",
    "                              input_shape=[28, 28, 1]))\n",
    "model.add(keras.layers.MaxPool2D(pool_size=(2, 2), strides=2, padding=\"SAME\"))\n",
    "model.add(keras.layers.ReLU())\n",
    "model.add(keras.layers.Conv2D(filters=64, kernel_size=(3, 3), padding=\"SAME\", strides=1))\n",
    "model.add(keras.layers.MaxPool2D(pool_size=(2, 2), strides=2, padding=\"SAME\"))\n",
    "model.add(keras.layers.ReLU())\n",
    "model.add(keras.layers.Flatten())\n",
    "model.add(keras.layers.Dense(1024))\n",
    "model.add(keras.layers.Dense(10, activation=keras.layers.Softmax()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_dataset, train_label), (test_dataset, test_label) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=keras.optimizers.Adam(lr=1e-3), \n",
    "              loss=tf.losses.SparseCategoricalCrossentropy(), metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 28, 28, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "re_lu (ReLU)                 (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 14, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "re_lu_1 (ReLU)               (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 3136)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              3212288   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 3,241,354\n",
      "Trainable params: 3,241,354\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "118/118 [==============================] - 7s 57ms/step - loss: 12.3324 - accuracy: 0.8313 - val_loss: 0.1905 - val_accuracy: 0.9450\n",
      "Epoch 2/30\n",
      "118/118 [==============================] - 7s 58ms/step - loss: 0.1572 - accuracy: 0.9536 - val_loss: 0.1485 - val_accuracy: 0.9567\n",
      "Epoch 3/30\n",
      "118/118 [==============================] - 6s 55ms/step - loss: 0.1149 - accuracy: 0.9654 - val_loss: 0.1321 - val_accuracy: 0.9635\n",
      "Epoch 4/30\n",
      "118/118 [==============================] - 6s 53ms/step - loss: 0.0962 - accuracy: 0.9712 - val_loss: 0.1245 - val_accuracy: 0.9655\n",
      "Epoch 5/30\n",
      "118/118 [==============================] - 6s 54ms/step - loss: 0.0831 - accuracy: 0.9748 - val_loss: 0.1145 - val_accuracy: 0.9692\n",
      "Epoch 6/30\n",
      "118/118 [==============================] - 7s 58ms/step - loss: 0.0726 - accuracy: 0.9772 - val_loss: 0.1062 - val_accuracy: 0.9729\n",
      "Epoch 7/30\n",
      "118/118 [==============================] - 6s 55ms/step - loss: 0.0662 - accuracy: 0.9800 - val_loss: 0.1091 - val_accuracy: 0.9732\n",
      "Epoch 8/30\n",
      "118/118 [==============================] - 6s 54ms/step - loss: 0.0601 - accuracy: 0.9823 - val_loss: 0.0989 - val_accuracy: 0.9738\n",
      "Epoch 9/30\n",
      "118/118 [==============================] - 7s 56ms/step - loss: 0.0567 - accuracy: 0.9824 - val_loss: 0.1183 - val_accuracy: 0.9702\n",
      "Epoch 10/30\n",
      "118/118 [==============================] - 6s 54ms/step - loss: 0.0494 - accuracy: 0.9846 - val_loss: 0.1197 - val_accuracy: 0.9746\n",
      "Epoch 11/30\n",
      "118/118 [==============================] - 6s 53ms/step - loss: 0.0468 - accuracy: 0.9856 - val_loss: 0.1118 - val_accuracy: 0.9740\n",
      "Epoch 12/30\n",
      "118/118 [==============================] - 7s 56ms/step - loss: 0.0430 - accuracy: 0.9869 - val_loss: 0.1048 - val_accuracy: 0.9753\n",
      "Epoch 13/30\n",
      "118/118 [==============================] - 6s 54ms/step - loss: 0.0470 - accuracy: 0.9854 - val_loss: 0.1014 - val_accuracy: 0.9762\n",
      "Epoch 14/30\n",
      "118/118 [==============================] - 6s 55ms/step - loss: 0.0404 - accuracy: 0.9877 - val_loss: 0.1088 - val_accuracy: 0.9759\n",
      "Epoch 15/30\n",
      "118/118 [==============================] - 7s 56ms/step - loss: 0.0483 - accuracy: 0.9853 - val_loss: 0.1311 - val_accuracy: 0.9700\n",
      "Epoch 16/30\n",
      "118/118 [==============================] - 6s 54ms/step - loss: 0.0447 - accuracy: 0.9864 - val_loss: 0.1157 - val_accuracy: 0.9755\n",
      "Epoch 17/30\n",
      "118/118 [==============================] - 7s 55ms/step - loss: 0.0423 - accuracy: 0.9870 - val_loss: 0.1272 - val_accuracy: 0.9748\n",
      "Epoch 18/30\n",
      "118/118 [==============================] - 6s 55ms/step - loss: 0.0329 - accuracy: 0.9895 - val_loss: 0.1227 - val_accuracy: 0.9738\n",
      "Epoch 19/30\n",
      "118/118 [==============================] - 6s 54ms/step - loss: 0.0380 - accuracy: 0.9889 - val_loss: 0.1223 - val_accuracy: 0.9748\n",
      "Epoch 20/30\n",
      "118/118 [==============================] - 6s 55ms/step - loss: 0.0452 - accuracy: 0.9869 - val_loss: 0.1349 - val_accuracy: 0.9767\n",
      "Epoch 21/30\n",
      "118/118 [==============================] - 6s 54ms/step - loss: 0.0400 - accuracy: 0.9887 - val_loss: 0.1312 - val_accuracy: 0.9748\n",
      "Epoch 22/30\n",
      "118/118 [==============================] - 6s 54ms/step - loss: 0.0385 - accuracy: 0.9893 - val_loss: 0.1356 - val_accuracy: 0.9756\n",
      "Epoch 23/30\n",
      "118/118 [==============================] - 6s 52ms/step - loss: 0.0452 - accuracy: 0.9883 - val_loss: 0.1253 - val_accuracy: 0.9780\n",
      "Epoch 24/30\n",
      "118/118 [==============================] - 6s 54ms/step - loss: 0.0541 - accuracy: 0.9866 - val_loss: 0.1289 - val_accuracy: 0.9791\n",
      "Epoch 25/30\n",
      "118/118 [==============================] - 6s 53ms/step - loss: 0.0377 - accuracy: 0.9901 - val_loss: 0.1199 - val_accuracy: 0.9789\n",
      "Epoch 26/30\n",
      "118/118 [==============================] - 6s 53ms/step - loss: 0.0354 - accuracy: 0.9909 - val_loss: 0.1173 - val_accuracy: 0.9792\n",
      "Epoch 27/30\n",
      "118/118 [==============================] - 6s 53ms/step - loss: 0.0426 - accuracy: 0.9891 - val_loss: 0.1240 - val_accuracy: 0.9796\n",
      "Epoch 28/30\n",
      "118/118 [==============================] - 6s 53ms/step - loss: 0.0345 - accuracy: 0.9907 - val_loss: 0.1335 - val_accuracy: 0.980956 - ac\n",
      "Epoch 29/30\n",
      "118/118 [==============================] - 6s 52ms/step - loss: 0.0295 - accuracy: 0.9918 - val_loss: 0.1615 - val_accuracy: 0.9769\n",
      "Epoch 30/30\n",
      "118/118 [==============================] - 6s 52ms/step - loss: 0.0300 - accuracy: 0.9918 - val_loss: 0.1503 - val_accuracy: 0.9771\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x = np.expand_dims(train_dataset, axis=3), y = train_label, batch_size=512,\n",
    "                    epochs=30, validation_data=(np.expand_dims(test_dataset, axis=3), test_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': [12.33238410949707,\n",
       "  0.15724468231201172,\n",
       "  0.11486787348985672,\n",
       "  0.09615559130907059,\n",
       "  0.08313044160604477,\n",
       "  0.07259340584278107,\n",
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       "  0.029542086645960808,\n",
       "  0.029951265081763268],\n",
       " 'accuracy': [0.8312666416168213,\n",
       "  0.9535833597183228,\n",
       "  0.9653666615486145,\n",
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       "  0.9918000102043152],\n",
       " 'val_loss': [0.19054371118545532,\n",
       "  0.1484835147857666,\n",
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       "  0.1118481457233429,\n",
       "  0.10482237488031387,\n",
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       "  0.13123507797718048,\n",
       "  0.1355968713760376,\n",
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       "  0.12886323034763336,\n",
       "  0.11991490423679352,\n",
       "  0.11732959002256393,\n",
       "  0.12402370572090149,\n",
       "  0.1334528625011444,\n",
       "  0.16145125031471252,\n",
       "  0.15027911961078644],\n",
       " 'val_accuracy': [0.9449999928474426,\n",
       "  0.9567000269889832,\n",
       "  0.9635000228881836,\n",
       "  0.965499997138977,\n",
       "  0.9692000150680542,\n",
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       "  0.9746000170707703,\n",
       "  0.9739999771118164,\n",
       "  0.9753000140190125,\n",
       "  0.9761999845504761,\n",
       "  0.9758999943733215,\n",
       "  0.9700000286102295,\n",
       "  0.9754999876022339,\n",
       "  0.9747999906539917,\n",
       "  0.973800003528595,\n",
       "  0.9747999906539917,\n",
       "  0.9767000079154968,\n",
       "  0.9747999906539917,\n",
       "  0.975600004196167,\n",
       "  0.9779999852180481,\n",
       "  0.9790999889373779,\n",
       "  0.9789000153541565,\n",
       "  0.979200005531311,\n",
       "  0.9796000123023987,\n",
       "  0.98089998960495,\n",
       "  0.9768999814987183,\n",
       "  0.9771000146865845]}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"MNIST-Classifier\", save_format=\"tf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = keras.models.load_model(\"MNIST-Classifier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "logs = history.history\n",
    "data2 = logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set()\n",
    "from matplotlib import pyplot as plt\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.rcParams['figure.dpi'] = 80 #分辨率\n",
    "plt.rcParams['font.sans-serif'] = ['Monaco']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = sns.lineplot(x = range(len(logs['loss'])), y = logs['loss'], label=\"loss in trainning\")\n",
    "ax = sns.lineplot(x = range(len(logs['loss'])), y = logs['val_loss'], label=\"loss in testing\")\n",
    "ax.set_title(\"Loss\")\n",
    "ax.set_xlabel(\"epoch\")\n",
    "ax.set_ylabel(\"loss value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = sns.lineplot(x = range(len(logs['loss'])), y = logs['accuracy'], label=\"accuracy in trainset\")\n",
    "ax = sns.lineplot(x = range(len(logs['loss'])), y = logs['val_accuracy'], label=\"accuracy in testset\")\n",
    "ax.set_title(\"accuracy\")\n",
    "ax.set_xlabel(\"epoch\")\n",
    "ax.set_ylabel(\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'loss value')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5y2T27Uth9uzHGTPmfv7+9zdxOh2MHHkv99wzGoCVK1fwyy8/ExFRk88/305kZC0effSvtG/foSim8SQk9CMxcTiffLKRjz5aR3x8ez766EPMZhN/+csU+vcfCMD58+d5/vl5pKTspV69evTrN5D16z/g4483X5Gr03nxBnVPP7t+V/TWr1/PoEGDMJtLbznVqVOHOnXqAHDttdcye/Zs+vbty4ULFwgJKV/zPisrz+OhhZx217m8U2dyiAozerSO6kBRXB+wzMycgO3pwS1YcvV1nk6nE6fTNcKJu+Zt/Pev7DlQ+VdDK1y89b19s7oM7XpTmZd1OFTsdidvvrmKvXv3sHLl37FYQnjmmdm89NILzJnzLLt372L79m2sWfMu0dHRHD9+DFV1Lfe3v71Ely7dWLHiTWw2GwcPppV4H537O8hud6LXO1FVlS1bPmHBgkVcf30D5s79KytWLGP27HlXLLt8+ZvcccdQPvnkM8LCQrDbnezfn8L58+f43/+O8d57HwOueOLjOzBjxuMYjSZef91VBN9++33AdUWk0+maz+Fwcv78OU6ePMG6dZs4dOggEyeOo2vXnlx33fU4nSo7d37D008/z/Tps1i37j2effZp1q/ffMm6XPvX6VQ5cCCNhIT+bNz4KTt3fs2zz86hU6euhIWF87e/vYTTqfLRR1s4dy6LRx99RHsvLuf+3ADFPrt6va7MDRi/umUhMzOTHTt2kJiYWK7ldDpdhboyUlXPfixF5/TyC+0er6O6/FTkfapuP8GSq6/zrE62bdvK2LHjiY6uQ3h4OBMnTuGLL7Zjs9kwGo1YrYX8+usv2O12YmLqU7/+dQAYjUZOnjxJRsYZzGYzrVvfUuZtDhw4hIYNG2M0GklI6E96+oFyxayqKhMmTMJsNmM2WwAYNGgoYWHhmEwmxo4dz6+/Hub8+XMlLq8oCuPHT8RoNHLzzS1p0OBGDh06qL3eokUrevTohV6vZ8CAwZw+fYpz50peV506dRk+/E4MBgM9evRGUXT8/vtRHA4HX375GePHTyA0NJSYmPrcfvvwMubn2WepSlt6drsdh8OB3W7H6XRSWFiITqfDaHS1kjZu3EjDhg1p1arVVdfz7bffct1111G/fn0yMjJ49tln6dKlC6GhoVWRhsZ98UpBoVy9KURFDe16U7laYWVVGb2UnDlzhnr1Lh5arFfvWhwOB5mZmdx6azz33fcgb7yxlN9/P0qHDp14+OFpREfX4fHHn2L16te57757qFUrkjvvHMngwX8q0zajoqK0x2azhQsXLpQr5po1a2GxWLTnDoeDN95Yxpdffsa5c+fQ6Vwnxc6fP0fNmrWuWD4ioqb23VxSDJfHB3DhQn6J11bUrh1V7LnFYiE/P59z57Kw2+3UrVtPe61u3WvKlWd5VWlLb/ny5bRu3ZoVK1aQlJRE69atmT17tvb6+vXrS2zlue/jc0tLS2PkyJHccsstJCYmEhkZyYIFC6okh0uFmF0tvQuF9irfthCi6tSpU4eTJ49rz0+ePFHUhZrryzwx8Q5WrvwH77+/EYfDwfLlrqsX69e/jqeeeoZNm7Yxbdqj/O1vL/C///1eqbGVdoP/5dO3b09ix44vWbhwKZ9++hXr1n0C+LbVXatWJAaDgdOnT2rTTp8+5dVtVmlL7+GHH+bhhx8u9fVNmzaVON19H5/b2LFjGTt2bKXHV14Xr96Ulp4Qgaxv3wG89dZqmjdvSUiIhRUrFtO7dwJGo5G0tJ9wOBzExjbHYrFgNlu0iy22bv2Ejh07ExlZmxo1aqAoSqVfuFOrViQ6nY5jx/5H06ZNSp0vPz8fk8lIzZq1KCwsZOXKZZUahyf0ej09evRm9erX+etf55KdfZ6PPvrQq9v0uwtZqhP3Ob0LVmnpCRHIRo0aS35+PuPGjcJut9OhQyceeWQmAHl5eSxevJDjx49hNBpp3bqNds9bSspuli17jYKCC0RFRTN9+mPExNSv1NgsFgujR9/P5MkTsNttzJ37QrHDkm79+w9iz55d3H77ACIiajJuXNV36FGSadMe5YUX5nH77QOoV+9a+vTpy6efbvHa9hTVWwNZVRNZWXkeH+/PuWBlyqv/pldcfUb1i63kyPyHokB0dA0yMgL7ikYInlx9nafT6eT06d+pW/d6r9+yENgjDxQXCLn+61/vkJy8k1dfvbIl6v7c3Hxzc7Ky8rXPrsFQTa/erG5Cii5kkZaeEEJ45ujRI6SnH0BVVX755Wc++OA9evTo5bXtyeHNCjAadBj0Orl6UwghPHThQgFz5jzBmTOnqFmzFn37DmDo0Nu9tj0pehUUYjZIS08IITwUG9uM995bX2Xbk8ObFRRqMcgtC0IIUU1I0augUItBblkQQohqQopeBYWYDRRIS08IIaoFKXoVFGoxckFaekIIUS1I0augULMBm92J3cORGoQQQlQdKXoVFGKRrsiECETDhw/h22+Tq2RbJ0+eJCGhG1artUq2Vx7btm1l8mT/6L2lMkjRqyCt/005ryeE8FC9evXYvv0bTCZTuZc9ceI4XbvGU1hYWOE49u9PYejQfsWm9e07gNdeK/8A3f5Kil4FhVpcfdzJeT0hhPB/UvQqKFQ7vCktPSEClc1mY+nSV7n99oEMGdKX5557mtzcXMA1WOvSpa8yZEhf+vbtwd13D2P//hQADhz4L+PHj6Zv3x4MHtyHuXOfLHH9l7fWJk16gJUrl/Pwww+SkNCdhx66j5MnT5S47IQJ9wEweHAfevXqoh2S3b17F/ffP4r+/Xty330j+e67iyPVJCVt5v/+788kJHQnMXEQ//rXO+Tm5jJjxhSyss6SkNCNhIRuHDnyK1u2bOKBB8Zoy3btGs+GDeu5++5h9OvXg3nzZmOz2bTX//Wvd/jznwcwZEhf3ntvLUOH9tPeD38gPbJUkPvw5gXpikyICinctwH74d1eWLMCuHomNjTsgLlt2QZxvdQ//vEm+/btYeXKv2OxhPDMM7N5+eUXmTPnWfbs+ZbPP9/GmjXvEh0dzfHjx3D3479w4QK6du3O66+vwWazcfBgWpm3mZS0mQULFnH99Q2YO/evrFy5nNmz510x34oVb3LHHUP55JPPCAsLwW53cuhQOnPnPskLL7xMq1at2bVrJ48/PoN33/0Qs9nMCy/M47XXVtCmTRzZ2ec5efIE4eHh/O1vr/L0039l48ZPtfX/978/XrHNr776nOXLV+N0OnnwwbF8+ukWBg/+E3v3fsvbb69h4cKl3HDDjSxb9mqpI7P7irT0KkhaekIEvm3btjJ27Hiio+sQHh7OxIlT+OKL7dhsNoxGI1ZrIb/++gt2u52YmPrUr38dAEajkZMnT5KRcQaz2Uzr1reUeZsDBw6hYcPGGI1GEhL6k55+oMzLbtiwnsGD/0SbNreg0+no0qUbTZo0ZdeufwNgMBj49dfD5OXlEhFRk6ZNm5Xr/Rg1aiw1a9YiMrI2nTt31WLbvv1T+vcfTJMmsZjNZh54YCJOp39d2S4tvQoKNbvO6cnVm0JUjLntnzxqhf2Ryhhu58yZM9SrF6M9r1fvWhwOB5mZmdx6azz33fcgb7yxlN9/P0qHDp14+OFpREfX4fHHn2L16te57757qFUrkjvvHMngwWXL0T0qO4DZbOHChQtljvfkyeOkpu5j48aLfVra7Xbi4zsQEhLCiy++wj//uZbly1+jSZNYJkyYRMuWrcu8/qio6GKxnT2bCUBGRgaNGzfVXgsLCycsLLzM660KUvQqyN3Sk/43hQhcderU4eTJ4zRp4vpCP3nyBDqdTitMiYl3kJh4B9nZ2cyf/yzLl7/G7NnPUL/+dTz11DOoqkpq6j6mTZvELbfcynXXXV9psSmKcsW0a66px91338v99z9Y4jLt2nWgXbsO2Gw23n//XZ566nHWr99c4rrKIzo6mtOnT2nP8/JyycvLrdA6K5sc3qygi+f0pOgJEaj69h3AW2+tJiMjg7y8XFasWEzv3gkYjUbS0n7ixx+/x2azYbFYMJst6HR6ALZu/YSsrLMoikKNGjVQFKXSB82tVSsSnU7HsWP/06YNHZrIxo0f8d13/8HpdFJYWMD+/SmcPn2Ks2cz+frrr8jPz8dgMBAaGqbFVLt2FDk52WRnZ3sUy2239SUpaTM//3wIq9XKypUrvD5IcHlJS6+C5OZ0IQLfqFFjyc/PZ9y4Udjtdjp06MQjj8wEIC8vj8WLF3L8+DGMRiOtW7dh5swnAEhJ2c2yZa9RUHCBqKhopk9/jJiY+pUam8ViYfTo+5k8eQJ2u425c1+gQ4dOPPnk0yxb9iq//XYEg8HAzTe3YNq0WTidKu+//y7PP/80qqpyww03MmfOswA0aHAjffsO4M47b8fpdLBixZpyxdKhQyfuvvtepk9/GIfDwciRowkLC/fo/kNvUVT3ZUZBKisrz+Pj/YoCljALdz25he5trmXMgOaVHJ1/UBSIjq5BRkYOgf5pCZZcfZ2n0+nk9OnfqVv3eq+3BCrjnF514W+5ZmdnM3Bgb9av30zdutdUeH3uz83NNzcnKytf++waDDoiI8PKtI4qbXeuXbuWxMREWrZsydSpU4u91rt3b1q3bk1cXBxxcXEMGjToqutKSkqiT58+tGnThtGjR3Ps2DFvhl6qEJPrMIbcsiCEELBjxxdYrVby8/NYvPgVmjdvUSkFr7JUadGrW7cuEydOZMSIESW+vmTJElJTU0lNTWXz5s2lrueXX37h8ccf5+mnn2b37t3ExsbyyCOPeCnqq9PrdZiMOhk9XQghgM2bNzJkSAKJiYPIzMzg6aef83VIxVTpOb2+ffsCkJaWRlZWlsfr2bhxI926daNr164ATJ48mU6dOnHo0CGaNGlSKbGWR4hJBpIVQgiA+fMX+TqEq/KrC1lmzZqF0+mkSZMmPPLII7Rt27bE+dLT02nVqpX2PDw8nBtuuMHjoufpVbru5dwDyVbwal+/5c4rUPO7VLDk6us8A/39Fd7n6WfIb4re/PnzadmyJQDr169n/PjxbNq0ifr1r7zSKT8/n4iIiGLTatSoQV5eXrm3W9aTn1cTHmokO89KdHSNCq/Ln0VFBXZ+lwqWXH2Vp9Pp5MwZHXq9gl7v/bMsBoN/XTbvTYGcq8OhotPpUBTF48+u3xS9+Ph47fHdd9/Nli1b+Prrr7nrrruumDc0NJScnJxi03JzcwkLK38By8rKw+HhALCK4vrSMOoV8i7YyMjI+eOFqiF3npmZgX1FIwRPrv6Qp05nICPjNBERkej1elx9ZFY+f7ui0ZsCO1eV3NxsFEWHTqcr9tnV68t+9abfFL3LKYpCaXdTNG3alLS0ix235uXlcfToUY/P51X0P72l6Jye06lWuEcDf6aqFX+vqotgydWXedaufQ05OefIzDyJu0Nob9DpdH7X/6O3BHquer2B2rXrAp5/dqu06NntdhwOB3a7vaiXgEJ0Oh1nzpzh+PHjtG7t6vvt448/5scff+S550q+6mfo0KEMHz6c5ORk2rZty+LFi4mNjfXJRSwAFpMeh1PFZndiMup9EoMQ1Y1Op6NmzdpERESW+gduRblatOFkZuYG/B8xwZCr69BmxdZRpUVv+fLlLFmyRHuelJTE7bffzrhx43jmmWc4evQoRqORRo0asWLFCm644QYAjh8/zqBBg9i8eTMxMTE0atSI559/ntmzZ5ORkUGbNm1YtGhRVaZSjNYVmdUhRU+IclIUxWtHSBQF9Ho9Op0uYAuBWzDlWhHSI0sFe2SJjq7B8nX/Ycuu33jhwY5cExlayRH6nq9776hKwZKr5Bl4giXXkvL02x5ZApW7V5YC6ZVFCCH8mhS9SmAxyUgLQghRHUjRqwQh5qL+N6UrMiGE8GtS9CqB+0IW6YpMCCH8mxS9SmDRzulJS08IIfyZFL1KoJ3Tk5aeEEL4NSl6lcB9Tq9AzukJIYRfk6JXCbSb0+WWBSGE8GtS9CqBnNMTQojqQYpeJTAb9SjIOT0hhPB3UvQqgaIoWMwGOacnhBB+TopeJbGY9HJOTwgh/JwUvUoSIi09IYTwe1L0KkmISS89sgghhJ+ToldJLGaDdDgthBB+TopeJbEUtfScgTyQlRBCVHNS9CpJSFFXZIVyiFMIIfyWFL1KYtG6IpOiJ4QQ/kqKXiWRgWSFEML/SdGrJDKQrBBC+D8pepXEfU5PDm8KIYT/qtKit3btWhITE2nZsiVTp07Vpv/6669MnDiRzp07Ex8fzz333MMPP/xQ6np2795Ns2bNiIuL035WrFhRFSmUSjqdFkII/2eoyo3VrVuXiRMnkpycTFZWljY9JyeHHj168Oyzz1KzZk3effddxo8fzxdffEFoaGiJ64qKimLnzp1VFfofssjwQkII4feqtKXXt29f+vTpQ2RkZLHprVu35v/+7/+oXbs2er2eUaNGceHCBQ4fPlyV4VVIiEnO6QkhhL+r0pZeWX3//fc4nU4aNGhQ6jznzp2jS5cumEwmunXrxrRp06hVq5ZH21MUz+J0L6coEGq5eJ+ep+vzV5fmGeiCJVfJM/AES64VzdPvil5WVhYzZ85k8uTJ1KhRo8R5GjZsyMcff0yjRo04deoUc+bMYdasWR6d14uMDKtoyERF1cCquhrNil5HdHTJcVd3UVGBmVdJgiVXyTPwBEuunubpV0UvJyeH+++/n27dujF+/PhS56tTpw516tQB4Nprr2X27Nn07duXCxcuEBISUq5tZmXl4XA4PYpXUVxvfGZmDhfyrQCcPXeBjIwcj9bnry7NM9B7WQuWXCXPwBMsuZaUp16vK3MDxm+KnrvgtWrViieffLJcy+p0OlRVRfVwT1f0A6KqYDEWXchitQfsB05VK/5eVRfBkqvkGXiCJVdP86zSC1nsdjuFhYXY7XacTieFhYXYbDZyc3MZN24cjRo14umnn/7D9Xz77bf873//Q1VVzpw5w7PPPkuXLl1KvdKzKhgNOvQ6hQK5elMIIfxWlbb0li9fzpIlS7TnSUlJ3H777XTo0IH//Oc/HDx4kKSkJO31lStXEh8fT0pKCuPHjyc1NRWAtLQ0HnvsMc6dO0dERATdunVjxowZVZlKiUJkeCEhhPBriurpMcEAkZWVh93u+Tm96OgaZGS4ji0/ujyZMIuROWPbVXKUvnV5noEsWHKVPANPsORaUp4GQ9nP6Uk3ZJUoxGyQ+/SEEMKPSdGrRBaTXrohE0IIPyZFrxK5WnpyIYsQQvgrKXqVyGLSY7M7sXt4358QQgjvkqJXiSwyvJAQQvg1KXqVyD2QrJzXE0II/yRFrxLJQLJCCOHfylX0PvnkE8aOHcuQIUMASElJ4bPPPvNKYNWRNqae3LYghBB+qcxF7+233+bll1+mQ4cOHD9+HICaNWuyatUqrwVX3bhHT5eBZIUQwj+VueitXbuWlStXMmHCBHQ612INGzbk119/9Vpw1U2I2X14U1p6Qgjhj8pc9LKysmjcuDEASqCPUugh9+jpck5PCCH8U5mL3o033sju3buLTduzZw8NGzas9KCqK/ctC9LptBBC+Kcyj7Lwl7/8hUmTJnH33Xdjs9lYtmwZa9euZf78+d6Mr1qxmN3n9KToCSGEPypzS69Hjx4sXryY9PR0rr32Wr799lvmzZtH165dvRlftSK3LAghhH8r13h6HTt2pGPHjt6KpdrTbk6XC1mEEMIvlbno/f7776W+dv3111dKMNWdWW5ZEEIIv1bmopeQkICiKLjHnL30Cs60tLTKj6wa0ut0mIw6uTldCCH8VJmL3ueff17s+alTp1i6dCl//vOfKzumai3EZJBzekII4afKXPTq169/xfOXXnqJ+++/X+uWTMhAskII4c8q1OF0RETEVc/1BSOL2SDn9IQQwk+VuaW3a9euYs8vXLjARx99RNOmTSs9qOosxKQn49wFX4chhBCiBGUuemPHji32PDQ0lJYtW/Lcc8+VeWNr165l/fr1pKenk5CQwMKFC7XX0tPTefLJJzl48CD169dn9uzZdOrU6arrev3118nNzaVr1648++yz1KxZs8yxeEuI2XVOT1VV6a5NCCH8TJmL3oEDByq8sbp16zJx4kSSk5PJysrSpttsNiZMmMCIESNYu3Ytn332GZMmTWLbtm1ERUVdsZ6dO3eyePFi3nzzTRo0aMBf//pX5syZw6JFiyocY0VZTHocThWb3YnJqPd1OEIIIS5RpYPI9u3blz59+hAZGVls+p49eygoKOCBBx7AZDIxcOBAmjRpQlJSUonrWb9+PYmJibRo0YLw8HCmTp3K9u3bycnJqYo0rurimHpyXk8IIfzNVVt6jz76aJlWUtH+Nw8dOkTTpk21IYsAmjdvTnp6eqnzd+/eXXt+4403YjQaOXz4MG3atCn39j09Cule7tLl3cMLFVrtKOEmz1bsZ0rKM1AFS66SZ+AJllwrmudVi55eXzWH5/Ly8oiIiCg2LSIigmPHjpU4f35+/hXz16hRg7y8vHJvOzIyrNzLXC4qqsbFx7VCATCHmomOrlHaItXSpXkGumDJVfIMPMGSq6d5XrXovfDCCx6ttLzCwsKuODSZk5NDWFjJBSk0NPSK+XNzc0ud/2qysvJwOJzlXg5cf2lERdUgMzOHoo5qcNpdhzVPnsqmliUwzumVlGegCpZcJc/AEyy5lpSnXq8rcwOmXB1Oe0uTJk1YuXIlTqdTO8SZlpbG4MGDS50/LS2NoUOHAvDbb79htVo9Htuvoh8QVb24Dssl/W8G2gfv0jwDXbDkKnkGnmDJ1dM8y1z0CgsLWbZsGf/+9785e/as1gcnwFdffVWmddjtdhwOB3a7HafTSWFhITqdjvbt22M2m1m1ahVjxozh888/Jz09nf79+5e4nsTERKZNm8aQIUNo0KABixYtIiEhgRo1fN+sD9EuZJFeWYQQwt+U+erNBQsWsHXrVoYMGUJWVhZjxozBaDQyYsSIMm9s+fLltG7dmhUrVpCUlETr1q2ZPXs2RqOR5cuXs337duLj43nttddYsmSJdrtCSkoKcXFx2nq6dOnCww8/zAMPPEDXrl2x2WzMnTu3HGl7j7ulJ12RCSGE/1FUtWwNxF69erFq1SoaNWpEu3bt2Lt3Lz/99BOLFi1i5cqV3o7Ta7Ky8rDbPT+nFx1dg4yMi8eWfz2RzTN/T2F4z0YM7NigEiP1nZLyDFTBkqvkGXiCJdeS8jQYyn5Or8wtvfPnz9OoUSPXQjodDoeDFi1a8J///KfcQQcyraUnhzeFEMLvlLno1alTh1OnTgGuERb279/Pzz//XGW3NVQXFlPROT3pdFoIIfxOmYveoEGD2L17NwAjRoxg7NixDBs2jD/96U9eC646CjHLOT0hhPBXZb56c/LkydrjO++8k2bNmpGTk0PXrl29Elh1ZTbqUUAGkhVCCD9U5pbeZ599hsNx8Yv8lltuoVu3bjKSwGUURXGNqSfn9IQQwu+UuaU3e/Zs9Ho9t99+O8OHD6dBg8C4MtEbLCa9nNMTQgg/VOaW3tdff80TTzzBTz/9xIABAxg1ahQbNmzAarV6M75qyTWmnrT0hBDC35S56BmNRgYOHMibb77Jtm3biI+PZ+HChXTr1s2b8VVLISa9nNMTQgg/5NF4erVq1SI6OppatWqRn59f2TFVe67Dm9LSE0IIf1OuDqf37t3LBx98wLZt27j++uvlloVSWMwGCqwOnKqKTi70EUIIv1Hmote3b18yMjLo378/a9asKdYXpiguxOQeSNahdUAthBDC98r8jTxu3DgGDRrk0Zh1weZiV2RS9IQQwp+U+ZzeiBEjpOCVkcU9vJCc1xNCCL/i0YUs4urcXZHJDepCCOFfpOh5gfucnty2IIQQ/kWKnhfIQLJCCOGfPC56+fn5FBYWVmYsAePiOT1p6QkhhD8pc9FbuHAh3333HQDJycl07NiRDh06sHPnTq8FV12FyECyQgjhl8pc9DZs2MBNN90EwPLly5k2bRpPPPEEixYt8lZs1ZY2kKyc0xNCCL9S5qKXk5NDREQEhYWF/Pe//2XkyJGMGDGCI0eOeDG86kkGkhVCCP9U5junw8PDOXXqFAcPHuTmm2/GaDRSWFiI0+n0ZnzVknZOT1p6QgjhV8pc9BITExkxYgRWq5WZM2cC8MMPP3DjjTdWSiCXd2tWWFhI9+7dWbFiRYnzx8bGEhISog1i27ZtW1atWlUpsVTUxVsWpKUnhBD+pMxFb8qUKbRr1w6j0Ui7du0AMJlMWgGsqNTUVO2xw+GgZ8+eDBgw4KrLfPjhhzRq1KhStl+ZjAYdep1CgVy9KYQQfqVcHUN27txZe/zbb78RGRlJ69atKz2ob775hvz8fPr161fp664qIWaDdEMmhBB+psxF7/HHH2fYsGHEx8ezefNmZsyYgaIozJ8/n8GDB1dqUOvXr2fQoEFYLJarzjd69GicTictW7Zk5syZNGnSxKPteTr6j3u5kpYPMespsDk8Xrc/uVqegSZYcpU8A0+w5FrRPBVVVdWyzNi1a1e2b99OSEgId9xxB/fddx/h4eHMnz+fTZs2ebb1Epw9e5bu3bvzzjvv0KZNm1Ln27NnD7fccgtWq5WVK1eyfv16tm7dSnh4eKXFUhEP/+1LCm0O3ni8j69DEUIIUaTMLb0LFy4QEhJCbm4uR44coV+/fuh0OqZOnVqpAW3atIkGDRpcteABtG/fHnCdV5w6dSobN25k//79dO/evVzby8rKw+Hw7ApURYGoqBpkZuZw+Z8ORr3C2fNWMjJyPFq3P7lanoEmWHKVPANPsORaUp56vY7IyLKNAlTmole7dm1++eUX0tPTadOmDTqdjvz8fO3qycqyfv16EhMTy72coiiUsdF6hYp+QFT1ynWEmA1csDoC6sNXUp6BKlhylTwDT7Dk6mmeZS56o0ePZtiwYQC88sorAOzdu5fGjRuXf6ul+Omnn/j555/505/+dNX5Dh06hNVqJTY2FpvNxqpVqygsLPSr0dwtJj02uxO7w4lBL/16CyGEPyhz0bvnnnvo1q0ber2e6667DoAbbriBuXPnVlow69evp0ePHkRHR1/xWlxcHCtXriQ+Pp7MzEyefvppTp48idlspmXLlqxevZqIiIhKi6WiLJcMLxQeIkVPCCH8QbluWWjQoAGZmZn88MMPxMTEaH1xVpbZs2eX+tql9/F17NiRpKSkSt12Zbu0K7LwEKOPoxFCCAHlKHq5ubk89thjfP7554DrHFrv3r158cUXqVGjhtcCrK5kIFkhhPA/ZT7uNn/+fLKzs/noo4/Yt28f69evJycnhwULFngzvmrLPZDsBemKTAgh/EaZW3pff/0169at0863NW/enJdffpnhw4d7LbjqTAaSFUII/1Pmll5BQcEVF4pERERQUFBQ6UEFghCzdDothBD+psxFr1WrVixatEgbSsjpdLJ48WJatmzpteCqs4ujp0tLTwgh/EWZD2/OmjWLsWPH8vHHHxMTE8OJEyfQ6/WsWbPGm/FVW9ro6dLptBBC+I0yF71GjRqxdetWvvzyS06ePMm1115Ljx49/KavS39jKbplQYqeEEL4j3LdpxcWFlbpIyoEKrllQQgh/M9Vi96rr75appVMmTKlUoIJJO6WnlzIIoQQ/uOqRS8lJeUPV1DZHU4HCu0+PbllQQgh/MZVi97bb79dVXEEHL1Oh8mok5vThRDCj0hPyF4UYjLIOT0hhPAjUvS8yGLSUyBXbwohhN+QoudFFrNBzukJIYQfkaLnRSEmvVy9KYQQfkSKnhdZis7pqZ6MaS+EEKLSSdHzohCzHodTxWZ3+joUIYQQSNHzKm14IbmCUwgh/IIUPS+62BWZnNcTQgh/IEXPi9y9shTIFZxCCOEXpOh5UYhZhhcSQgh/4jdFb9asWbRs2ZK4uDjt5/jx46XOn56ezogRI2jTpg0DBw5k165dVRht2VhkIFkhhPArflP0AMaMGUNqaqr2ExMTU+J8NpuNCRMm0Lt3b/bu3cukSZOYNGkSmZmZVRzx1WkDyco5PSGE8AvlGk/PX+zZs4eCggIeeOABdDodAwcO5B//+AdJSUmMHDmy3OvzdKAI93KlLR9quTi8UHUejOKP8gwkwZKr5Bl4giXXiubpV0Xv/fff5/3336devXrce++9DB8+vMT5Dh06RNOmTdHpLjZUmzdvTnp6erm3GRkZ5nG8blFRNUqcXi/f1cLTGwxER5c8T3VSWp6BKFhylTwDT7Dk6mmeflP0Ro0axaOPPkpERAQpKSlMmTKFGjVq0K9fvyvmzcvLIyIioti0iIgIjh07Vu7tZmXl4XB4dvO4orje+MzMHErqdKXwQiEAGVl5ZGTkeLQNf/BHeQaSYMlV8gw8wZJrSXnq9boyN2D8pui1aNFCe9yxY0dGjhxJUlJSiUUvLCyMnJziRSQnJ4ewMM9abRX9gKhqyeswG91XbzoC4kNYWp6BKFhylTwDT7Dk6mmefnUhy6V0Ol2pfVY2adKE9PR0nM6LLbS0tDSaNm1aVeGVSYjZfZ+eXMgihBD+wG+K3pYtW8jNzcXpdJKSksLatWtJSEgocd727dtjNptZtWoVVquVrVu3kp6eTv/+/as46qszG/UoyC0LQgjhL/ym6L3zzjv07NmTtm3bMmfOHB555BEGDRqkvT5o0CA2btwIgNFoZPny5Wzfvp34+Hhee+01lixZQlRUlK/CL5GiKFjMerllQQgh/ITfnNN75513rvr65s2biz2PjY3lgw8+8GZIlcJikoFkhRDCX/hNSy9QhZgN0uG0EEL4CSl6XmYx6eWcnhBC+Akpel4WYtJLh9NCCOEnpOh5mcVsoMDqwBkMN84IIYSfk6LnZe6BZAvlEKcQQvicFD0vk+GFhBDCf0jR8zKLDCQrhBB+Q4qel2ldkUlLTwghfE6KnpfJQLJCCOE/pOh5WYhJOp0WQgh/IUXPyy6e05PDm0II4WtS9LxMa+nJ4U0hhPA5KXpedvGcnrT0hBDC16ToeZkMJCuEEP5Dip6Xuc/pyS0LQgjhe1L0vMx9Tk9uWRBCCN+ToudlBr0OvU6hQK7eFEIIn5Oi52WKohBiNkg3ZEII4Qek6FUBGUhWCCH8gxS9KmAxGeScnhBC+AEpelUgxKyXWxaEEMIPGHwdgJvVamXu3Lns2rWLrKwsYmJimDBhAkOGDClx/tjYWEJCQlAUBYC2bduyatWqqgy5zEKKRk8XQgjhW35T9Ox2O3Xr1uXvf/871113Hfv27ePBBx/kuuuuIy4ursRlPvzwQxo1alTFkZafxaTHandidzgx6KVxLYQQvuI3RS80NJQpU6Zoz+Pj47n11ltJTU0ttehVlqLGosfL/dHyIUU3qBfaHBgN1a/olTXPQBAsuUqegSdYcq1onn5T9C6Xn5/Pjz/+yL333lvqPKNHj8bpdNKyZUtmzpxJkyZNyr2dyMiwioQJQFRUjatvo2YIACFhFqJrh1Z4e77yR3kGkmDJVfIMPMGSq6d5+mXRczqdzJo1i1atWtG1a9cS53n77be55ZZbsFqtrFy5kvvuu4+tW7cSHh5erm1lZeXhcDg9ilNRXG98ZmYOqnqVGZ2u9R8/eR69s/qd2ytzngEgWHKVPANPsORaUp56va7MDRi/K3qqqjJnzhxOnz7N6tWrtQtVLte+fXsATCYTU6dOZePGjezfv5/u3bt7sM0KhYyqXn0dFmNRV2SF9mr9YfyjPANJsOQqeQaeYMnV0zz9quipqsrcuXNJS0vjrbfeIiys7IceFUVB9dM9LQPJCiGEf/Crojdv3jy+++473nrrrasepjx06BBWq5XY2FhsNhurVq2isLDQ6xe8eCpEG2lB7tUTQghf8ptLCY8dO8a7777Lzz//TM+ePYmLiyMuLo4VK1YAEBcXR0pKCgCZmZlMnz6d+Ph4evbsyX/+8x9Wr15NRESEL1MolUUbPV1aekII4Ut+09KrX78+Bw8eLPX11NRU7XHHjh1JSkqqirAqRYh79HTplUUIIXzKb1p6gcxivnghixBCCN+RolcF3C09ObwphBC+JUWvCrhbenIhixBC+JYUvSrgvpBFblkQQgjfkqJXBfQ6HSajTg5vCiGEj0nRqyIykKwQQvieFL0qEmKSgWSFEMLXpOhVEYvZIOf0hBDCx6ToVZEQk16u3hRCCB+ToldFLCYDBVaH33aKLYQQwUCKXhUJMetxOFVsds/G7hNCCFFxUvSqiMUsvbIIIYSvSdGrItoN6nJeTwghfEaKXhXR+t+UKziFEMJnpOhVkRCzDC8khBC+JkWvishAskII4XtS9KqIxT2QrJzTE0IIn5GiV0VC3MMLyeFNIYTwGSl6VSREblkQQgifk6JXReSWBSGE8D2DrwOozpw5meQc24vNbkSxRKCEFP0YTFfMq53Tk1sWhBDCZ/yq6GVnZzN79my+/vprwsLCGDduHGPGjClx3j179jBv3jx+//13GjduzHPPPUezZs2qNN7C77aQ+9PnV75gtGgFUBcSgWKJwGQOp5v5NM6ff2NLxnfojCYMJiMGowmjyYTBZMJoNmMymzCZLZgtJkxmM0ajEaPRiN5kxGgwYNTr0OmUKs1TiOpGVVVwOsBpw3FBQS3MR1X0oNOBogdFQVGq5v+RqjpBdYLTCVza924J29diKvqtOsFhQ3U6wGEDhx3VaQeH/YrpOO3knQ3HnmdD1RlQdAbQG0FvQNEXPdZd+ljveo+09bjW4Vq/A4q2ozrtrvkA9EYUvREMRb/1RjCYtMeKTu/Fd7Jy+FXRmzdvHlarlW+++YZjx44xZswYbrrpJnr06FFsvqysLCZOnMiTTz7JwIEDeeedd3jooYf49NNPMZmubGV5i6XDCKJaduDcqVM487NRL1z2k30G++nDrg8uMDysaMGc8m9LBQpUhTwUHOhxoMN56Y+iR0UBFO3/i+L+Vyn+2P2aquhQUVAVHSg6VHSoiuJ6XPQcRQc6HT/rDTidTtfiRatRuPh/9IrpqKA6UXCiqCqK6ih6XPQc1xeBUvSDoqAqeu2L6eJj129Vpy/6stKh6nQoTieKakPntKM4HShOO4pqd/2+7AdUVMXgWodOj6ozgM793ACKe5rr+TmzEZvNfRhaKcqt6B285ItSe6wUZXzpF5Z7GsXncX352VAcrh8c1qLfNhSHtei3reiLzOr6kta5vrjQGVGLfqM3FP0YtS8zdAZARXE6QFVdX7ZFj1Edrve56DGqk1y9DofDCegu2YEXY7/4G20eBVzruPhRKkZB5dJXrqwrSmkvlO6KTtrV4kWgqADgtKO6C4DDtd/hKv/dij7bKEWFUKdDUXQX8y57gOB0oqpF77WzqMi5f6rQhSrdWgkUnasI6gyu97bkmS55WPT/KqQmoUNmoZhCvB6i3xS9/Px8kpKSWL9+PeHh4cTGxjJixAg+/PDDK4re9u3bueGGG/jzn/8MwJgxY3jrrbdITk6mZ8+e5d62p3/wKSYzode25UJkzpX/L4uoqhO1IA+1IBs1PxvVXohqt2GzWrEWFmIrLMRus2K3WnHYbDhsVhx2G067DdVuA6cDteiLnaIvMUV1uL7oi77IFBzoVAeKqgKqFot7RAfX15B6WYyur2MdTvSo6BQVV5lzun4rFx/rlbKNDOHUZnN99TlwFVWHqhQVZ8X1o7oeO9DhVF2FQUFFr7hicf12LeF+7I6x+PYU7Oiwq3psRb/t2m89dtWCHT0qFK3XgQEneqzolQIMODEoDvQ4MShO9DgwKq4vKXOZMq4Yq6rHpuqxocemGi55HIa1KAcFFYPiwFgUqwE7RqVQi92AA2PRb/cBAKfq2ruXvv+uPzOK9oHq2g9Ff/pof6S4/h9c8lzbkxd/u6mlFIXLp1++HFz8yiv22mWrK239bg5V59rH6C95HIKdMBzu/Y8eu6rHqSgoqnrF5/rS34qiFn3m1BJjvhrV/blGQS16b4vWWvy1otcvX7tyyZquXDdaPu4/di997Ch6bC+KXq8DvWov+ky7PhcG7XPvfu4sWsp5cX2qe326S9Z9yWP14mfRgAOjtl679vkz4MCgOjDaXNOVKzIpKWcXm9VIM1XBXIbvYsWDv5su5TdF78iRI6iqStOmTbVpzZo1Y9u2bVfMm56eTvPmzbXniqIQGxtLenp6uYteZGTYH8/0B6KiavzBHDWBmApvpzKpqorTqeJUVRxO92Ncv52q6/Wi1xwOJ06nE6eqan/IOlXX/Np6tGkXl1fVoobFZYVYBVBB53oRd6F2AI5L5y967FTRWi6q04nitONUDK6WYNG8RbMUe160QVTApoKt6LE7JkparujxxRxULR7VqRbFU7SsqqI6XcXC9VXmmq67pHigqiiK6zVFdbWeHDoDTkUP6Iq2f/EL4fI/WNDiLWnaxURVV4BFLbWLf2GX1tAq/mG4+H5p2yj+S9tvqjtv976/5LG2v93TtXVftv+1x+5tFP+D7NI/1lBLn1ZWatGfVVd7Hyr7QGepIV7SEL50m4pSfKJy2SLa5839mOLvY6nv4VXevxLfFy2gkmPX/i8UrVz7/3Ppa+44L9m+e6OX73v39FrhFrrUidQ65i+LP/7eLZnfFL38/HzCw8OLTYuIiCAvL6/EeWvWrFlsWo0aNUqc949kZeUVHeIpP0VxvfGZmaW39Ko7HaBXIKpuREDn6RYM+xQkz0BU3XPNzblAbhlO/ZSUp16vK3MDxm+KXmho6BVFKycnh7CwKxMJDQ0lJ6f4u5Obm1vivGVR0Q/IxRZCYAuWPCF4cpU8A0+w5Oppnn5zn96NN94IwKFDh7RpaWlpNGnS5Ip5mzZtSlpamvZcVVUOHjxY7NCoEEIIcTm/KXqhoaH069ePhQsXkpubS3p6OuvWrWPYsGFXzJuQkMBvv/3Ghg0bsFqt/P3vfwegc+fOVR22EEKIasRvih7AnDlzMBgMdOvWjfvuu4/x48drV27GxcWRkpICQGRkJEuXLmXlypXEx8ezadMmli9fXqW3KwghhKh+FFUNhqO/pcvKysNu9/xClujoGmRkVM8Tx2UVLHlC8OQqeQaeYMm1pDwNhrJfyOJXLT0hhBDCm6ToCSGECBpS9IQQQgQNv7lPz1f0+orX/cpYR3UQLHlC8OQqeQaeYMn10jzLk3PQX8gihBAieATHnwRCCCEEUvSEEEIEESl6QgghgoYUPSGEEEFDip4QQoigIUVPCCFE0JCiJ4QQImhI0RNCCBE0pOgJIYQIGlL0hBBCBA0pekIIIYKGFD0PZWdnM2XKFOLi4ujatStvvfWWr0PyilmzZtGyZUvi4uK0n+PHj/s6rApbu3YtiYmJtGzZkqlTpxZ7LT09nREjRtCmTRsGDhzIrl27fBRl5bharr1796Z169bavh00aJCPoqw4q9XKX//6V3r37q3lsmnTJu31QNmvf5RnIO3T2bNn061bN2699VZ69+7NihUrtNc83p+q8Mj06dPVCRMmqDk5OeqBAwfUjh07ql999ZWvw6p0jz32mLpgwQJfh1HpPv30U3X79u3q3Llz1UceeUSbbrVa1V69eqnLly9XCwsL1c2bN6u33nqrmpGR4cNoK6a0XFVVVXv16qXu2LHDR5FVrry8PHXRokXq0aNHVafTqe7du1e99dZb1f379wfUfr1anqoaWPv00KFD6oULF1RVVdXjx4+rAwYMUDdv3lyh/SktPQ/k5+eTlJTE1KlTCQ8PJzY2lhEjRvDhhx/6OjRRRn379qVPnz5ERkYWm75nzx4KCgp44IEHMJlMDBw4kCZNmpCUlOSjSCuutFwDTWhoKFOmTOH6669HURTi4+O59dZbSU1NDaj9erU8A03jxo2xWCzac51Ox9GjRyu0P6XoeeDIkSOoqkrTpk21ac2aNePQoUM+jMp73n//fdq3b8/QoUNZt26dr8PxqkOHDtG0aVN0uov/NZo3b056eroPo/KuWbNm0bFjR0aNGsW+fft8HU6lyc/P58cff6RJkyYBvV8vzdMtkPbpyy+/zC233ELPnj3Jz89n6NChFdqfUvQ8kJ+fT3h4eLFpERER5OXl+Sgi7xk1ahRJSUkkJyfzxBNPsGDBAj799FNfh+U1eXl5REREFJsWqPsWYP78+XzxxRd89dVXDBgwgPHjx3Ps2DFfh1VhTqeTWbNm0apVK7p27Rqw+/XyPCHw9un06dNJTU3lgw8+YMiQIdp+83R/StHzQGho6BVvbk5ODmFhYT6KyHtatGhB7dq1MRgMdOzYkZEjR1bLQ0JlFRYWRk5OTrFpgbpvAeLj47FYLFgsFu6++25uvvlmvv76a1+HVSGqqjJnzhxOnz7NwoULURQlIPdrSXlCYO5TRVFo3bo1JpOJxYsXV2h/StHzwI033ghQ7HBmWlpascMLgUqn06Gqqq/D8JomTZqQnp6O0+nUpqWlpRU7lB3IFEWp1vtXVVXmzp1LWloaq1at0r4EA22/lpZnSar7Pr2Uw+Hg6NGjFdqfUvQ8EBoaSr9+/Vi4cCG5ubmkp6ezbt06hg0b5uvQKt2WLVvIzc3F6XSSkpLC2rVrSUhI8HVYFWa32yksLMRut+N0OiksLMRms9G+fXvMZjOrVq3CarWydetW0tPT6d+/v69D9lhpuR4/fpyUlBSsVitWq5X333+fH3/8UTtMVh3NmzeP7777jtWrVxc7BRFo+7W0PANpn54/f56PP/5Y+/7Zt28f//znP+ncuXOF9qeiBsqfAFUsOzubJ598km+++YawsDDGjRvHmDFjfB1WpRs5ciQHDx7E4XAQExPDqFGjuPPOO30dVoUtXryYJUuWFJt2++238+KLL3Lw4EGefPJJDh48SP369Xnqqafo1KmTjyKtuNJyHTduHNOnT+fo0aMYjUYaNWrEI488QocOHXwUacUcO3aM3r17YzKZMBgM2vQHH3yQCRMmBMx+vVqeffr0CZh9mp2dzaRJk0hLS8Nut3PNNdeQmJjI+PHjURTF4/0pRU8IIUTQkMObQgghgoYUPSGEEEFDip4QQoigIUVPCCFE0JCiJ4QQImhI0RNCCBE0pOgJIa6wfv16unfv7uswhKh0UvSEEEIEDSl6QgghgoYUPSH8VGFhIa+88gp9+vShXbt2jBw5kv/+97+Aq2uxu+66i5dffplOnTrRpUsXXnrpJWw2m7b8L7/8wrhx4+jQoQPdu3fnqaeeKtYzfUFBAQsXLqRfv37ExcVx22238dFHHxWL4b333qN37960bduWyZMnk5ubWzXJC+ElUvSE8FNz5szh+++/Z+3atezatYsBAwZw//33k52dDcD333+PXq9nx44d/OMf/2Dbtm2sWrUKgNzcXMaMGUPjxo356quvWLduHYcPH+axxx7T1v/kk0+yc+dOli1bxv79+3n33XeL9VKfkZHB4cOH2bJlC1u3buXAgQO8+eabVfsmCFHJpOgJ4YeysrL46KOPeOqpp6hXrx4Gg4F77rmHGjVq8OWXXwJQs2ZNHn74YUwmE40aNeL+++/nww8/BOCrr77CZrMxY8YMQkJCqFu3Lk888QSff/45Z86c4ezZs2zatIk5c+bQqFEjFEXhmmuuoUWLFloMer2emTNnYrFYqFu3Lrfddhvff/+9T94PISqL4Y9nEUJUtaNHjwIwYsSIYtNtNhunTp0CoF69euj1eu216667jpMnTwJw4sQJYmJiivXCf8MNN2ivud10002lxhAZGYnRaNSelzR4shDVjRQ9IfxQdHQ0ABs3biQmJuaK1xcvXszJkydxOBxa4Tt27BjXXHMNANdeey0nTpzAbrdrhe/333/XXnMvc+TIEVq2bOn1fITwF3J4Uwg/VL9+ffr06cO8efM4duwY4DpPt2PHDk6fPg24BtlcunQpVquVw4cPs3r1ahITEwHo2bMner2eV155hYKCAs6cOcPzzz9Pr169qFOnDrVr12bw4MHMmzePw4cPA3D69Gl++ukn3yQsRBWRoieEn3r55Zdp0aIF9913H3FxcfTv358PPvgA9xCYrVu3xmaz0b17d+655x769OnDAw88AEB4eDhr1qzh4MGD9OjRg8TERBo0aMBLL72krf+ZZ56hXbt2jB8/nri4OO666y4OHTrkk1yFqCoyiKwQ1dDixYtJTk7mn//8p69DEaJakZaeEEKIoCFFTwghRNCQw5tCCCGChrT0hBBCBA0pekIIIYKGFD0hhBBBQ4qeEEKIoCFFTwghRNCQoieEECJoSNETQggRNP4fheOx5PpE/t4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(x = range(len(logs['loss'])), y = logs['loss'], label=\"loss in trainning\")\n",
    "ax = sns.lineplot(x = range(len(logs['loss'])), y = logs['val_loss'], label=\"loss in testing\")\n",
    "ax.set_title(\"With Dense(1024) Layer Loss\")\n",
    "ax.set_xlabel(\"epoch\")\n",
    "ax.set_ylabel(\"loss value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'accuracy')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(x = range(len(logs['loss'])), y = logs['accuracy'], label=\"accuracy in trainset\")\n",
    "ax = sns.lineplot(x = range(len(logs['loss'])), y = logs['val_accuracy'], label=\"accuracy in testset\")\n",
    "ax.set_title(\"With Dense(1024) Layer accuracy\")\n",
    "ax.set_xlabel(\"epoch\")\n",
    "ax.set_ylabel(\"accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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        "plotlyServerURL": "https://plot.ly"
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   "source": [
    "line1 = go.Scatter(x = np.arange(len(logs['loss'])) + 1, y = data1['accuracy'], \n",
    "                         name=\"accuracy in trainset with dense1024\")\n",
    "line2 = go.Scatter(x = np.arange(len(logs['loss'])) + 1, y = data2['accuracy'], \n",
    "                         name=\"accuracy in trainset without dense1024\")         \n",
    "fig = go.Figure([line1, line2])\n",
    "fig.update_layout(xaxis_title=\"epoch\", yaxis_title=\"accuracy\")\n",
    "fig.show()"
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   "source": [
    "line1 = go.Scatter(x = np.arange(len(logs['loss'])) + 1, y = data1['loss'], \n",
    "                         name=\"train loss in trainset with dense1024\")\n",
    "line2 = go.Scatter(x = np.arange(len(logs['loss'])) + 1, y = data2['loss'], \n",
    "                         name=\"train loss in trainset without dense1024\")         \n",
    "fig = go.Figure([line1, line2])\n",
    "fig.update_layout(xaxis_title=\"epoch\", yaxis_title=\"loss\")\n",
    "fig.show()"
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  {
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